Course Information

Ani Adhikari

Table of Contents

• Course Information
  • Community Standards
  • Videos, Textbook, and Lectures
  • Helpful References
  • Weekly Class Sessions
  • Weekly Support Sessions
  • Study Guides
  • The Required Components of Your Work
    • Wednesday Section Attendance
    • Weekly Assignments
    • Exams
  • Grades
  • Collaboration and Integrity

Welcome to Data 140, Fall 2026! This page contains the nitty-gritty details of the class. Please read it carefully, and also read the companion page about the class.

Enrolled and waitlisted students will be added to our Ed forum.

Community Standards

The class and its Ed forum are formal, academic spaces. Posts in the Ed forum must directly relate to the course and be in alignment with Berkeley’s Principles of Community and the Berkeley Campus Code of Student Conduct. We expect all posts to demonstrate appropriate respect and consideration for others. Please be friendly and thoughtful; our community draws from a wide spectrum of experiences. Posts that violate these standards will be removed.

Videos, Textbook, and Lectures

The textbook is Probability for Data Science by Ani Adhikari and Jim Pitman.

Videos are embedded in the textbook. For access, use your CalNet credentials to open YouTube. The videos consist of explanations of selected portions of the written text, typically those that require detailed calculation or discussion.

There’s more in the written text that you are expected to work through. In fact, you can learn the material by studying only the written text, as many students have done in the past.

The lectures are versions of the textbook content and videos. They will follow the textbook sections in sequence according to the calendar on the homepage. Students are expected to attend. Lectures are partly driven by conversation that arises from students’ questions and responses, and the examples used in lecture might be different from those in the textbook.

Lectures will not assume that you have already worked through the content of the lecture. But some students find it useful to skim the relevant sections or even just the subsection headings before coming to lecture.

Helpful References

• Probability by Jim Pitman, published by Springer NY. Available for Berkeley students on SpringerLink at no cost or low cost (for a printed version) if you log on via CalNet.
• Introduction to Probability by Joe Blitzstein and Jessica Hwang
• Introduction to Probability by Dimitri Bertsekas and John Tsitsiklis
• Theory Meets Data, the Data 88S textbook written by Prof. Adhikari, covers some of the basic concepts of Data 140 at a more elementary level.

Weekly Class Sessions

• Lectures: 2 PM to 3:30 PM on Tuesdays and Thursdays in Dwinelle 155. You are expected to attend.
• Sections: Wednesdays in the Gateway; 35-40 students per section. You must enroll in one Wednesday section, and attendance in that section is required. Typically, Wednesday sections will be a workout in Tuesday’s lecture content.
• Mega sections (formally Supplemental Sections): Fridays in VLSB and Morgan; 90-100 students per mega section. You must enroll in one Friday mega section, which can be at a different time from your Wednesday section. You are expected to attend. Typically, Friday mega sections will be a workout in Thursday’s lecture content.

Sections and mega-sections will be conversations about exercises and may sometimes include portions of the weekly lab. They will cover how to select appropriate methods, similarities and differences between exercises, etc. You must attend the discussion section and the mega-section in which you are enrolled. You cannot attend other sections.

Each session assumes that you have attended the previous sessions. For example, each section assumes that you attended lecture on the previous day. TAs will not re-teach what was covered in lecture.

Student participation and informal conversation will be encouraged during all lectures and sections. You are expected to attend and participate. To help make this possible, the class does not allow time conflicts.

Weekly Support Sessions

• Assignment “parties” (work sessions with staff support): Lab Party and Homework Party days and times TBA.
• Office hours by the instructor and course staff, days and times to be reflected on the Weekly Schedule.

Study Guides

The weekly Study Guide is intended to help you distribute your work effectively over the week. It will provide you with an outline of the week’s main ideas, connections to the textbook, and a schedule that describes the focus of all the week’s sessions.

You are welcome to follow a different study schedule if that suits you better. But leaving all the work till the weekend is not a good idea. As past students have advised in Study Tips, it is important to work regularly. This is not a bingeable class.

The Required Components of Your Work

Wednesday Section Attendance

Wednesday sections will include a worksheet designed to get you started on the week’s homework. Your responsibility is to fill out the worksheet in collaboration with your section and your TA, who will then collect your filled worksheet and scan it into Gradescope.

Weekly Assignments

Data 140 is primarily a math class. Your main tools for working will be paper-and-pencil or a tablet equivalent. All assignments will involve both math and computing. You will do them on paper and in Jupyter notebooks.

There will be a homework assignment and a lab each week. Assignments will be released on Monday night and will be due by 5PM on the following Monday. Sometimes there will be changes in release dates or due dates because of exams and holidays. We’ll let you know.

The week’s content, sessions, homework, and lab are carefully coordinated. Each week’s assignments are based on the material of that week, not of the previous week.

You are allowed one lab partner from among students in the class. You may have different partners for different labs. Logistical details will be posted on Ed.

Assignments must be submitted on Gradescope. Please follow all submission instructions. Not doing so will result in no credit and no regrade request allowed for the work. It is your responsibility to make sure your submission is complete.

Late assignments will not be accepted. But if you have DSP accommodations for extended time on assignments, please make sure we have received your DSP accommodation letter. We will contact you about arrangements.

Assignments will primarily be graded for effort. We will spot check randomly selected problems for correctness. There is plenty of support available while you work on them, so if you get started early and use the support then you should be able to turn in work that you understand well and know to be correct. That is by far the most efficient way to succeed in the class.

Exams

All of these will be in person and proctored.

• Midterm 1 on Monday, September 28, starting at 8 PM and ending by 10 PM. Rooms TBA.
• Midterm 2 on Monday, November 2, starting at 8 PM and ending by 10 PM. Rooms TBA.
• Final Exam on Tuesday, December 15, 8 AM to 11 AM, Exam Group 5. Rooms TBA. It is your responsibility to make sure you are not enrolled in another class that has a conflicting final exam.

There will be no alternate exams except as required by campus rules. If you have extended time accommodations for tests from DSP, please make sure that you have enough available hours around the times of the regularly scheduled exams.

Grades

Each Wednesday’s section attendance score will be 1 for satisfactory completion and timely submission of the worksheet, and 0 otherwise. The first Wednesday section will be in Week 2 and will be used as a practice run of the worksheet process, with no score involved.

• The lowest 3 of your remaining 11 Wednesday section scores will be dropped in the calculation of your attendance score.

We strongly recommend that you turn in all assignments even if you can only complete some of them partially. However, to give you some leeway in case of illness and emergencies, we will drop the following in the calculation of your overall score:

• your two lowest homework scores
• your lowest lab score

Minimum Average Score Required on Exams: In order to pass the class, the first condition you must satisfy is to have an unweighted average percentage score of at least 25% on the two midterms and the final, after the clobber (see below) has been applied. Students whose average exam score falls below this threshold will not pass the class.

If you satisfy the condition above, your course grade (whether passing or non-passing) will be based on your overall score in the class, calculated using the following weights:

• Wednesday section attendance: 6%
• Homework 12%
• Labs 8%
• Midterm 1 18%
• Midterm 2 18%
• Final 38%

Clobber policy: If your final exam percentage score is greater than either of your midterm percentage scores then it will replace the lower of your two midterm percentage scores (only your Midterm 1 score will be replaced if both your midterm scores are equal). Here “percentage score” on an exam means your score on the exam as a percent of the total points on the exam.

Letter grades will be based on a combination of absolute cutoffs and the distribution of overall scores. Towards the end of the term, we will make three guarantees: “An overall score of at least x will result in a grade of at least C-; at least y will result in at least B-; at least z will result in at least A-“. The thresholds x, y, and z will depend partly on this term’s exams and performance.

Collaboration and Integrity

You are encouraged to discuss practice problems, homework, and labs with your fellow students and with course staff. Arguing with friends about exercises is an excellent and time-honored way to learn. However, you must write up all your own assignments and code by yourself; for labs, this will be in collaboration with your lab partner if you have one.

Copying assignments from other sources is not only dishonest, it also doesn’t help anyone. Each exercise requires its own combination of ideas, and each student needs practice in coming up with those combinations, or else they will be at a loss when trying to use probability theory in their future work. From a purely practical perspective, all students must work independently on Data 140 exams – no collaboration allowed. If a test is the first time a student works independently, then the test is not likely to go well.

Data/Stat/Prob 140 materials including solutions and exams are the intellectual property of the course developers. From the campus statement on Academic Integrity: “… students may not circulate or post materials (handouts, exams, syllabi,–any class materials) from their classes without the written permission of the instructor.”

We are tough with dishonest students and we hope that we will not be put in that situation in this class. We expect that you will work with integrity and with respect for other members of the class, just as the course staff will work with integrity and with respect for you.